Enhancing Precision Healthcare Machine Learning For Advanced Diagnostics And Personalized Treatment

Abstract

Machine learning (ML) in precision healthcare has the potential to revolutionize diagnostic precision and tailor individualized therapy. This study examines the process of creating sophisticated ML techniques aimed at enhancing the accuracy of medical diagnoses and tailoring individual treatment approaches. By integrating large datasets—from genetic data, and medical images, to patient history, and real time monitoring—these models can outperform traditional methods in identifying patterns and predicting outcomes. It analyzes the potential of multiple ML algorithms (such as deep learning, reinforcement learning, and ensemble methods), applied in the construction of predictive models that aid in early detection of the disease, treatment and outcome prediction. It also reviews the challenges such as data privacy, transparency of algorithms, and healthcare infrastructure that must be addressed for successful deployment of ML in healthcare settings. Synthesizing the research, it highlights the need for collaborative efforts among various fields to realize the true potential of ML in precision health advancement.